PYTHON

np.where

>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.where(a < 5, a, 10*a)
array([ 0,  1,  2,  3,  4, 50, 60, 70, 80, 90])

numpy where

import numpy as np

# Return elements chosen from x or y depending on condition.
a = np.arange(10) # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
single_where = np.where((a<5),-1,a) # [-1, -1, -1, -1, -1,  5,  6,  7,  8,  9]
multiple_where = np.where((a<5),-1,np.where((a>5),0,a)) # [-1, -1, -1, -1, -1,  5,  0,  0,  0,  0]

numpy.where

Parameters:
condition : When True, yield x, otherwise yield y.
x, y : Values from which to choose. x, y and condition need to be broadcastable to some shape.

Returns:
out : [ndarray or tuple of ndarrays] If both x and y are specified, the output array contains elements of x where condition is True, and elements from y elsewhere.

np.where

np.where([[True, False], [True, True]],
...          [[1, 2], [3, 4]],
...          [[9, 8], [7, 6]])
array([[1, 8],
       [3, 4]])

numpy.where

>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.where(a < 5, a, 10*a)
array([ 0,  1,  2,  3,  4, 50, 60, 70, 80, 90])